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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Ibnu Utomo Wahyu Mulyono
Dian Nuswantoro University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Triple layer image security using bit-shift, chaos, and stream encryption Ajib Susanto; De Rosal Ignatius Moses Setiadi; Eko Hari Rachmawanto; Ibnu Utomo Wahyu Mulyono; Christy Atika Sari; Md Kamruzzaman Sarker; Musfiqur Rahman Sazal
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (851.826 KB) | DOI: 10.11591/eei.v9i3.2001

Abstract

One popular image security technique is image encryption. This research proposes an image encryption technique that consists of three encryption layers, i.e. bit-shift encryption, chaos-based encryption, and stream encryption. The chaos algorithm used is Arnold's chaotic map, while the stream cipher algorithm used is RC4. Each layer has different cryptology characteristics in order to obtain safer image encryption. The characteristics of cryptology are permutation, confusion, diffusion, and substitution. The combination of the proposed encryption method aims to secure images against various attacks, especially attacks on statistics and differentials. The encryption method testing is done by various measuring instruments such as statistical analysis, i.e. entropy information, avalanche effect, and histogram, differential analysis, i.e. UACI and NPCR, visual analysis using PSNR and SSIM, and bit error ratio. Based on the results of experiments that the encryption method that we propose can work excellently based on various measurement instruments. The decryption process can also work perfectly this is evidenced by the ∞ value based on PSNR, and zero value based on SSIM and BER.
Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation Ajib Susanto; Ibnu Utomo Wahyu Mulyono; Christy Atika Sari; Eko Hari Rachmawanto; De Rosal Ignatius Moses Setiadi; Md Kamruzzaman Sarker
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1448-1458

Abstract

Although numerous studies have been conducted on handwritten recognition, there is little and non-optimal research on Javanese script recognition due to its limitation to basic characters. Therefore, this research proposes the design of a handwritten Javanese Script recognition method based on twelve layers deep convolutional neural network (DCNN), consisting of four convolutions, two pooling, and five fully connected (FC) layers, with SoftMax classifiers. Five FC layers were proposed in this research to conduct the learning process in stages to achieve better learning outcomes. Due to the limited number of images in the Javanese script dataset, an augmentation process is needed to improve recognition performance. This method obtained 99.65% accuracy using seven types of geometric augmentation and the proposed DCNN model for 120 Javanese script character classes. It consists of 20 basic characters plus 100 others from the compound of basic and vowels characters.
Co-Authors Ajib Susanto Christy Atika Sari De Rosal Ignatius Moses Setiadi Eko Hari Rachmawanto Md Kamruzzaman Sarker Md Kamruzzaman Sarker Musfiqur Rahman Sazal